Machine learning is a branch of computer science, a field of artificial intelligence. It is a data analysis method that further helps automate the construction of the analytical model. Alternatively, as the word suggests, it provides machines (computer systems) with the ability to learn from data, without outside help to make decisions with minimal human interference. With the evolution of new technologies, machine learning has changed a lot in recent years.
Let’s discuss what is Big Data?
Big data means too much information and analysis means analyzing a large amount of data to filter the information. A human cannot perform this task efficiently within a time limit. So here is the point where machine learning for big data analytics comes into play. Let’s take an example, let’s say you are a business owner and you need to collect a large amount of information, which is very difficult on its own. Then you start to find a clue that will help you in your business or make decisions faster. Here you realize that you are dealing with immense information. Your analyzes need a little help for the search to be successful. In the machine learning process, the more data you provide to the system, the more the system can learn from it and it will return all the information you were looking for and thus make your search successful. This is why it works so well with big data analytics. Without big data, it cannot function at its optimal level due to the fact that with less data, the system has few examples to learn from. So we can say that big data has an important role in machine learning.
Instead of several advantages of machine learning in analytics, there are also several challenges. Let’s discuss them one by one:
- Learning from Big Data: With the advancement of technology, the amount of data we process is increasing day by day. In November 2017, Google was found to process approx. 25 PB per day, over time, businesses will cross these petabytes of data. The main attribute of data is volume. So it is a great challenge to process such a large amount of information. To overcome this challenge, distributed frameworks with parallel computing should be preferred.
- Learning different types of data: Today, there is a great variety of data. Variety is also an important attribute of big data. Structured, unstructured and semi-structured are three different types of data that also result in the generation of heterogeneous, non-linear and high-dimensional data. Learning from such a good data set is challenging and also results in increased data complexity. To overcome this challenge, you must use data integration.
- High speed transmitted data learning: There are various tasks that include the completion of work in a certain period of time. Speed is also one of the main attributes of big data. If the task is not completed in a specific period of time, the results of the processing may become less valuable or even useless. To do this, you can take the example of stock market prediction, earthquake prediction, etc. Therefore, it is a very necessary and challenging task to process the big data on time. To overcome this challenge, an online learning approach must be used.
- Learning from ambiguous and incomplete data: Previously, machine learning algorithms were given relatively more accurate data. So the results were also accurate at the time. But today, there is an ambiguity in the data because the data is generated from different sources that are also uncertain and incomplete. Therefore, it is a great challenge for machine learning in big data analytics. An example of uncertain data is data that is generated on wireless networks due to noise, shadows, fading, etc. To overcome this challenge, a distribution-based approach must be used.
- Low-value density data learning: The main goal of machine learning for big data analytics is to extract the useful information from a large amount of data for business gain. Value is one of the main attributes of data. Finding the significant value of large volumes of data that have a low value density is a big challenge. Therefore, it is a great challenge for machine learning in big data analytics. To overcome this challenge, data mining and knowledge discovery technologies must be used in databases.